# Team:Stockholm/model-development

<!doctype html>iGEM Stockholm Model Development

# Model Development

## P2 genetic switch

We started modeling the P2 genetic switch by describing the system graphically in terms of C protein dynamics, based on a similar approach applied to bacteriophage λ, as described by Mestivier et al. (3)

Based on the graphical representation, we derived differential equations for each of the reactions that the C protein is involved in. Notation: x - concentration of C; y - concentration of C2 (c protein dimer).

$$\frac{dx}{dt}=-k_0x$$

2. Dimerization of C protein

$$\frac{dx}{dt}=-k_{12}x^2+2k_{21}y;\ \frac{dy}{dt}=k_{12}x^2-k_{21}y$$

3. Synthesis of C protein

$$\frac{dx}{dt}=nk_tD_0P$$

4. Binding of C dimer to Pc promoter at low concentrations

$$\frac{dy}{dt}=-k_1yD_0+k_{-1}D_1;\ \frac{dD_0}{dt}=-k_1yD_0+k_{-1}D_1;\ \frac{dD_1}{dt}=k_1yD_0-k_{-1}D_1$$

5. Stimulated synthesis of C protein

$$\frac{dx}{dt}=nk_{t2}D_1P$$

6. Binding of C dimer to Pc promoter at high concentrations

$$\frac{dy}{dt}=-k_2yD_1+k_{-2}D_2;\ \frac{dD_1}{dt}=-k_2yD_1+k_{-2}D_2;\ \frac{dD_2}{dt}=k_2yD_1-k_{-2}D_2\$$

We then gathered all differential equations into one model, describing the dynamics of C protein:

$$\frac{dx}{dt}=-k_0-k_{12}x^2+2k_{21}y+nk_tD_0P+nk_{t2}D_1P$$

Following that, we made an assumption that dimerization and binding to the operator sites are faster chemical reactions than synthesis, and for these reactions:

$$\frac{dx}{dt}=0$$

This assumption allowed us to neglect some parts of the main equation, as well as derive expressions of some constants and species involved in the reactions. Specifically, for reaction #2 (dimerization):

$$\frac{dx}{dt}=-k_{12}x^2+2k_{21}y=0;\ \frac{dy}{dt}=\ k_{12}x^2-k_{21}y=0\rightarrow k_{12}x^2=k_{21}y\rightarrow y=\ \frac{k_{12}}{k_{21}}x^2=K_Dx^2$$

For reaction #4 (binding):

$$\frac{dy}{dt}=-k_1yD_0+k_{-1}D_1\rightarrow D_1=\frac{k_1}{k_{-1}}yD_0\rightarrow\ D_1=K_1D_0y\rightarrow D_1=K_1D_0\left(K_Dx^2\right)=K_1K_DD_0x^2$$

For reaction #6 (binding):

$$\frac{dy}{dt}=-k_2yD_1+k_{-2}D_2=0\rightarrow\ k_2yD_1=k_{-2}D_2\rightarrow D_2=\frac{k_2}{k_{-2}}yD_1=K_2yD_1=K_D^2K_1K_2x^4D_0$$

Another assumption we made is that when C dimer binds to D0, the affinity constant of C dimer to D0 changes by a factor σ , that is

$${\ K}_2=σK_1$$

This results in expression:

$$D_2=K_D^2σK_1^2x^4D_0$$

The general model of C protein concentration now looks like this:

$$\frac{dx}{dt}=-k_0x+nk_tD_0P+nk_{t2}D_1P$$

We make yet another assumption, that total phage DNA concentration is constant, and derive an expression of D0.

$$D=D_0+D_1+D_2=D_0+K_1K_DD_0x^2+σK_1^2K_D^2x^4D_0$$

Inserting this expression into the general model equation results in an updated model:

$$\frac{dx}{dt}=-k_0x+nk_tD_0P+nk_{t2}K_1K_DD_0x^2P=nD_0P\left(k_t+k_{t2}K_1K_Dx^2\right)-k_0x=\frac{nDP\left(k_t+k_{t2}K_1K_2K_Dx^2\right)}{1+K_1K_Dx^2+σK_1^2K_D^2x^4}-k_0x$$

First we relate the two transcription parameters:

$$k_{t2}=τk_t$$

Then we introduce a new parameter:

$$w=nk_tDP$$

The updated general model now looks like this:

$$\frac{1}{w}\frac{dx}{dt}=\frac{1+τK_1K_Dx^2}{1+K_1K_Dx^2+σK_1^2K_D^2x^4}-\frac{k_0}{w}x$$

Here we introduce a new time unit:

$$t\prime=wt$$

As well as two new parameters:

$$\gamma=\frac{k_0}{w}; u=K_DK_1$$

The final model is:

$$\frac{dx}{dt\prime}=\frac{1+\tau u x^2}{1+ux^2+\sigma u^2x^4}-\gamma x=f(x)$$

We use this equation to simulate C protein dynamics over time using python. To compute the concentration of species x (C protein) versus time t, we use ‘Euler scheme of numerical integration’. We set the initial conditions of species x and the parameter dt to compute small variation in concentration of C protein (dx) over a small time interval (dt) using f (x) ; the variation being f (x).dt.

$$dx/dt = f(x)$$

Hence, the concentration of protein C at a time t + dt is the concentration x at a time t + f(x). dt x(t + dt) = x(t) + dt. f (x) , where x(t) = concentration of x at time t. Having x(t + dt), we can compute the small variation of species x during the next time interval dt, and compute x ((t + dt) + dt) i.e. x(t + 2.dt) and thus follow an iterative procedure further to plot time evolution of x in this case. We also find steady states of our system by plotting f (x) over x. The system achieves a steady state, when C protein would not have a net new concentration i.e. change in x in small time interval dt would be zero.

Hence, the concentration of protein C at a time t + dt is the concentration x at a time t + f(x). dt

$$x(t + dt) = x(t) + dt. f (x)$$

where x(t) = concentration of x at time t. Having x(t + dt), we can compute the small variation of species x during the next time interval dt, and compute x ((t + dt) + dt) i.e. x(t + 2.dt) and thus follow an iterative procedure further to plot time evolution of x in this case.

We also find steady states of our system by plotting f (x) over x. The system achieves a steady state, when C protein would not have a net new concentration i.e. change in x in small time interval dt would be zero.

$$dx/dt =0, f(x)=0$$

Any value of x that nullifies f(x) would be a steady state concentration. The concentration of x at which f(x) crosses the horizontal axis in the plot thus gives us the steady state concentration.

In order to adapt the above general model to a stochastic setting, we use Poisson τ-leap approach - a simple technique to introduce noise into a deterministic model. As described by Tian et al, it is assumed that a number of reactions are occuring in a relatively large time interval interval [t, t +τ). The number of these reactions corresponds to a sample value generated from a Poisson distribution with a mean of a:

$$P(a_j\left({x}\right)\tau); a_j({x})\tau$$

Once this value is generated, the system can be updated in the following way:

$${x}\left(t+\tau\right)={x}\left(t\right)+\sum_{j=1}^{M}{v_jP(a_j\left({x}\right)τ)}$$

We adopt this approach to derive our general stochastic model with a τ value of 0.01. For our general model, the updated equation looks like this:

$$x\left(t+\tau\right)={x}\left(t\right)+\mathcal{P}\left[\frac{1+\tau u x^2}{1+ux^2+\sigma u^2x^4}\tau\right]-\mathcal{P}\left[\gamma x\tau\right]$$

We implement this approach in Python, using numpy.random.poisson function.

### Model Plasmid

Based on the graphical representation, we have derived the following differential equations. Notation: x - concentration of C; y- concentration of C2; m - concentration of Cox; n - concentration of Cox4.

1. Synthesis of C protein

$$\frac{dx}{dt}=nk_{t1}D_1P$$

$$\frac{dx}{dt}=-k_0x$$

3. Binding of arabinose

$$\frac{d[arabinose]}{dt}=-k_1D_0[arabinose]+k_{-1}D_1$$

$$\frac{dD_0}{dt}=-k_1D_0[arabinose]+k{-1}D_1$$

$$\frac{dD_1}{dt}=k_1D_0[arabinose]-k{-1}D_1$$

4. Dimerization of C protein

$$\frac{dx}{dt}=-k_{12}x^2+2k_{21}y$$

$$\frac{dy}{dt}=k_{12}x^2-k_{21}y$$

5. Synthesis of Cox from Pe promoter

$$\frac{dm}{dt}=Nk_{t2}D_1P$$

6. Tetramerization of Cox

$$\frac{dm}{dt}=-k_{14}m^4+4k_{41}n$$

$$\frac{dn}{dt}=k_{14}m^4-k_{41}n$$

$$\frac{dm}{dt}=-k_0m$$

8. Binding of C2 at low concentrations to Pe promoter

$$\frac{dy}{dt}=-k_2D_1y+k_{-2}D_3$$

$$\frac{dD_1}{dt}=-k_2D_1y+k_{-2}D_3$$

$$\frac{dD_3}{dt}=k_2D_1y-k_{-2}D_3$$

9. Slowed synthesis of Cox because of C2 binding

$$\frac{dm}{dt}=Nk_{t3}D_3P$$

10. Binding of Cox4 at low concentrations to Pe promoter

$$\frac{dn}{dt}=-k_3D_1n+k_{-3}D_2$$

$$\frac{dD_1}{dt}=-k_3D_1n+k_{-3}D_2$$

$$\frac{dD_2}{dt}=k_3D_1n-k_{-3}D_2$$

11. Slowed synthesis of Cox because of Cox4 binding to Pe promoter

$$\frac{dm}{dt}=Nk_{t4}D_2P$$

12. Binding of C2 at high concentrations to Pe

$$\frac{dy}{dt}=-k_4D_3y+k_{-4}D_5$$

$$\frac{dD_3}{dt}=-k_4D_3y+k_{-4}D_5$$

$$\frac{dD_5}{dt}=k_4D_3y-k_{-4}D_5$$

13. Binding of Cox4 leading to inhibited synthesis

$$\frac{dn}{dt}=-k_5D_3n+k_{-5}D_6$$

$$\frac{dD_3}{dt}=-k_5D_3n+k_{-5}D_6$$

$$\frac{dD_6}{dt}=k_5D_3n-k_{-5}D_6$$

14. Binding of C2 leading to inhibited synthesis

$$\frac{dy}{dt}=-k_6D_2y+k_{-6}D_6$$

$$\frac{dD_2}{dt}=-k_6D_2y+k_{-6}D_6$$

$$\frac{dD_6}{dt}=k_6D_2y-k_{-6}D_6$$

15. Binding of Cox4 at high concentrations to Pe

$$\frac{dn}{dt}=-k_7D_2n+k_{-7}D_4$$

$$\frac{dD_2}{dt}=-k_7D_2n+k_{-7}D_4$$

$$\frac{dD_4}{dt}=k_7D_2n-k_{-7}D_4$$

We then make an assumption that dimerization, tetramerization, and binding are very fast chemical reactions, faster than synthesis. For these reactions:

From reaction #3 we derive:

$$k_1D_0[arabinose]=k_{-1}D_1$$

$$D_1=\frac{k_1}{k_{-1}}D_0[arabinose]=K_1D_0[arabinose]$$

From reaction #4 we derive:

$$k_{12}x^2=k_{21}y$$

$$y=\frac{k_{12}}{k_{21}}x^2=K_Dx^2$$

From reaction #6 we derive:

$$k_{14}m^4=k_{41}n$$

$$n=\frac{k_{14}}{k_{41}}m^4={K_Tm}^4$$

From reaction #8 we derive:

$$k_2D_1y=k_{-2}D_3$$

$$D_3=\frac{k_2}{k_{-2}}D_1y=K_2K_1D_0[arabinose]K_Dx^2$$

From reaction #10 we derive:

$$k_3D_1n=k_{-3}D_2$$

$$D_2=\frac{k_3}{k_{-3}}D_1n=K_3K_1D_0[arabinose]K_Tm^4$$

From reaction #12 we derive:

$$k_4D_3y=k_{-4}D_5$$

$$D_5=\frac{k_4}{k_{-4}}D_3y=K_4K_2K_1D_0[arabinose]K_D^2m^4$$

From reaction #13 we derive:

$$k_5D_3n=k_{-5}D_6$$

$$D_6=\frac{k_5}{k_{-5}}D_3n=K_5K_2K_1D_0[arabinose]K_Dx^2K_Tm^4$$

From reaction #14 we derive:

$$k_6D_2y=k_{-6}D_6$$

$$D_6=\frac{k_6}{k_{-6}}D_2y=K_6K_3K_1D_0[arabinose]K_Dx^2K_Tm^4$$

From reaction #15 we derive:

$$k_7D_2n=k_{-7}D_4$$

$$D_4=\frac{k_7}{k_{-7}}D_2n=K_7K_3K_1D_0[arabinose]K_T^2m^4$$

The general models with these equations plugged in look like this:

$$\frac{dx}{dt}=Nk_{t1}D_1P-k_0x=Nk_{t1}K_1D_0[arabinose]P-k_0x$$

$$\frac{dm}{dt}=Nk_{t2}D_1P-k_{02}m+Nk_{t3}D_3P+Nk_{t4}D_2P=Nk_{t2}K_1D_0[arabinose]P-k_{02}m+Nk_{t3}K_2K_1D_0[arabinose]K_Dx^2P+Nk_{t4}K_3K_1D_0[arabinose]K_Tm^4P$$

The total plasmid concentration stays the same:

$$D=D_0+D_1+D_2+D_3+D_4+D_5+D_6=D_0+K_1D_0[arabinose]+K_3K_1D_0[arabinose]K_Tm^4+K_2K_1D_0[arabinose]K_Dx^2+K_7K_3K_1D_0[arabinose]K_T^2m^4+K_4K_2K_1D_0[arabinose]K_D^2m^4+K_6K_3K_1D_0[arabinose]K_Dx^2K_Tm^4$$

We introduce new parameters:

$$w=NDP$$

$$\alpha=\frac{k_0}{w};\ \beta=\frac{k_{02}}{w}$$

The final models are:

$$\frac{dx}{dt}=\frac{K_{t1}K_1[arabinose]}{1+[arabinose](K_1+K_3K_1m^4+K_2K_1K_Dx^2+K_7K_3K_5K_T^2m^8+K_4K_2K_1K_D^2x^4+K_5K_2K_1K_Dx^2K_Tm^4)}-αx$$

$$\frac{dm}{dt}=\frac{[arabinose](K_{t2}K_1+k_{t3}K_1K_2K_Dx^2+k_{t4}K_1K_3K_Tm^4)}{1+[arabinose](K_1+K_3K_1m^4+K_2K_1K_Dx^2+K_7K_3K_5K_T^2m^8+K_4K_2K_1K_D^2x^4+K_5K_2K_1K_Dx^2K_Tm^4)}-βm$$

### Switch Plasmid

Based on the graphical representation, we have derived the following differential equations for each of the reactions in the network. Notation: x - concentration of C; m - concentration of Cox; r - concentrattion of tetR, q - concentration of tetR2.

1. Synthesis of C protein

$$\frac{dx}{dt}=Nk_{t1}D_0P$$

$$\frac{dx}{dt}=-k_0x$$

3. Synthesis of Cox and tetR

$$\frac{dm}{dt}=Nk_{t2}D_1P$$

$$\frac{dr}{dt}=Nk_{t2}D_1P$$

$$\frac{dm}{dt}=-k_{02}m$$

5. Dimerization of tetR

$$\frac{dr}{dt}=-k_{12}r^2+2k_{21}q$$

$$\frac{dq}{dt}=k_{12}r^2-k_{21}q$$

6. Binding of tetR2 to D0

$$\frac{dq}{dt}=-k_1D_0q+k_{-1}D_2$$

$$\frac{dD_0}{dt}=-k_1D_0q+k_{-1}D_2$$

$$\frac{dD_2}{dt}=k_1D_0q-k_{-1}D_2$$

7. Slowed synthesis of C

$$\frac{dx}{dt}=Nk_{t3}D_2P$$

8. Binding of tetR2 to D2

$$\frac{dq}{dt}=-k_2D_2q+k_{-2}D_3$$

$$\frac{dD_2}{dt}=-k_2D_2q+k_{-2}D_3$$

$$\frac{dD_3}{dt}=k_2D_2q-k_{-2}D_3-k_{-2}D_3$$

9. Binding of arabinose

$$\frac{dD_0}{dt}=-k_3D_0[arabinose]+k-3D1$$

$$\frac{dD_1}{dt}=k_3D_0[arabinose]-k-3D1$$

$$\frac{dr}{dt}=-k_{03}r$$

Then we gathered all the equations into one per each protein involved (general models).

$$\frac{dx}{dt}=Nk_{t1}D_0P-k_0x+Nk_{t3}D_2P$$

$$\frac{dm}{dt}=Nk_{t2}D_1P+k_{02}m$$

$$\frac{dr}{dt}=Nk_{t2}D_1P-k_{12}r^2$$

$$\frac{dq}{dt}=k_{12}r^2-k_{21}q-k_1D_0q-k_{-1}D_2-k_2D_2q+k_{-2}D_3$$

We made an assumption that dimerization and binding are very fast chemical reactions, faster than synthesis. Hence, reaction #5, #6, #8 and #9 are equal to 0.

From reaction #5 we derive:

$$\frac{dr}{dt}=-k_{12}r^2+2k_{21}q=0$$

$$k_{12}r^2=k_{21}q$$

$$q=\frac{k_{12}}{k_{21}}r^2={K_Dr}^2$$

From reaction #6 we derive:

$$\frac{dq}{dt}=-k_1D_0q+k_{-1}D_2=0$$

$$k_1D_0q=k_{-1}D_2$$

$$D_2=\frac{k_1}{k_{-1}}D_0q=K_1D_0q=K_1K_DD_0r^2$$

From reaction #8 we derive:

$$\frac{dq}{dt}=-k_2D_2q+k_{-2}D_3=0$$

$$k_2D_2q=k_{-2}D_3$$

$$D_3=K_2K_1K_DD_0r^2K_Dr^2=K_D^2K_2K_1D_0r^4$$

From reaction #9 we derive:

$$\frac{dD_0}{dt}=-k_3D_0[arabinose]+k_{-3}D_1=0$$

$$k_3D_0[arabinose]=k_{-3}D_1$$

$$D_1=\frac{k_3}{k_{-3}}D_0[arabinose]=K_3D_0[arabinose]$$

Filling in these equations into the general model equations yields:

$$\frac{dx}{dt}=Nk_{t1}D_0P-k_0x+Nk_{t3}D_2P=Nk_{t1}D_0P-k_0x+Nk_{t3}PK_1K_Dr^2D_0$$

$$\frac{dm}{dt}=Nk_{t2}D_1P+k_{02}m=Nk_{t2}K_3D_0[arabinose]P-k_{02}m$$

$$\frac{dr}{dt}=Nk_{t2}D_1P-k_{12}r^2=Nk_{t2}K_3D_0[arabinose]P-k_{03}r$$

The total plasmid concentration stays the same:

$$D=D_0+D_1+D_2+D_3=D_0+K_3[arabinose]D_0+K_1K_Dr^2D_0+K_D^2K_1K_2D_0r^4$$

$$D_0=\frac{D}{D_1+K_3[arabinose]+K_1K_Dr^2+K_D^2K_1K_2r^4}$$

Updated general model now looks like this:

$$\frac{dx}{dt}=\frac{Nk_{t1}DP+Nk_{t3}PK_1K_Dr^2D}{1+K_3[arabinose]+K_1K_Dr^2+K_D^2K_1K_2r^4}-\frac{k_0}{w}x$$

We introduce new parameters:

$$w=NDP$$

$$\alpha=\frac{k_0}{w};\ \beta=\frac{k_{02}}{w};\gamma=\frac{k_{03}}{w}$$

The final models are:

$$\frac{dx}{dt}=\frac{k_{t1}+k_{t3}K_1K_Dr^2}{1+K_3[arabinose]+K_1K_Dr^2+K_D^2K_1K_2r^4}-αx$$

$$\frac{dm}{dt}=\frac{k_{t2}K_3[arabinose]}{1+K_3[arabinose]+K_1K_Dr^2+K_D^2K_1K_2r^4}-βx$$

$$\frac{dr}{dt}=\frac{k_{t2}K_3[arabinose]}{1+K_3[arabinose]+K_1K_Dr^2+K_D^2K_1K_2r^4}-γx$$